Convert LSTM univariate Autoencoder to multivariate Autoencoder
If each of your three features is a scalar then my first attempt would be to combine them into a vector for each step in the sequence. So instead of LSTM(128, input_shape=(30,1)) for a length-30 univariate sequence you would say LSTM(128, input_shape=(30,3)) for a multivariate (3) sequence. Similarly your output would become TimeDistributed(Dense(3, activation='linear')).
If your input features are each a vector then you should consider switching from the sequential API to the functional API. You would need three separate Input objects followed by a Concatenate layer before the LSTM layer. You would have three Dense layers to be your corresponding outputs. I tried adapting your code:
in0 = Input(shape=(trainX0.shape[1], trainX0.shape[2]))
in1 = Input(shape=(trainX1.shape[1], trainX1.shape[2]))
in2 = Input(shape=(trainX2.shape[1], trainX2.shape[2]))
concat = Concatenate()([in0, in1, in2])
lstm_enc = LSTM(128)(concat)
lstm_enc_drop = Dropout(rate=0.2)(lstm_enc)
rep = RepeatVector(trainX0.shape[1])(lstm_enc_drop)
lstm_dec = LSTM(128, return_sequences=True)(rep)
lstm_dec_drop = Dropout(rate=0.2)(lstm_dec)
out0 = TimeDistributed(Dense(trainX0.shape[2], activation='linear'))(lstm_dec_drop)
out1 = TimeDistributed(Dense(trainX1.shape[2], activation='linear'))(lstm_dec_drop)
out2 = TimeDistributed(Dense(trainX2.shape[2], activation='linear'))(lstm_dec_drop)
model = Model(inputs=[in0, in1, in2], outputs=[out0, out1, out2])
model.compile(optimizer='adam', loss='mae')
model.summary()